This invention relates generally to computed tomographic (CT) imaging, and more particularly to methods and apparatus for noise reduction in CT imaging applications.
Dose reduction has been the focus of research for x-ray CT for many years. By reducing the noise in the reconstructed images, either or both of the x-ray intensity used for scanning or the duration of a scan can be reduced, thereby reducing the dose to a patient while maintaining the same image quality as a scan without the benefit of noise reduction. It is well known that noise reduction can be performed either in projection space or in image space. Speckle reduction methods based on anisotropic diffusion have been proposed to reduce noise, but these methods are computationally intensive and are not able to keep up with the high frame rates of today's scanners.
Improvements of the speckle reductions methods were later proposed as a multi-scale version of anisotropic diffusion. A dyadic wavelet method has been used in some CT imaging applications to decompose the image into different image scales. Although these previously proposed methods are sufficient for ultrasound applications, they cannot be implemented directly for CT applications. One of the major obstacles to implementation of the methods in CT is the estimation of the noise property in the image. The anisotropic diffusion method uses an equation written as:
                                          ∂                          I              ⁡                              (                                  x                  ,                  y                  ,                  t                                )                                                          ∂            t                          =                  div          ⁡                      [                                          d                ⁡                                  (                                                                                ∇                      I                                                                            )                                            ·                              ∇                I                                      ]                                              (        1        )            where ∥∇I∥ denotes the local gradient, and d(∥∇I∥) the diffusivity function. The function d(∥∇I∥) should be monotonically decreasing so that diffusion decreases as the gradient strength increases. One such function is
                              d          ⁡                      (            u            )                          =                  ⅇ                      -                                          u                2                                            2                ⁢                                  σ                  n                  2                                                                                        (        2        )            The parameter σn depends upon the noise in the image. In ultrasound imaging, the noise can be either a constant for a particular type of the clinical exam, or the minimum standard deviation measured across the entire image. However, the use of a constant as a noise estimate does not yield satisfactory results for CT because noise changes significantly in CT from pixel to pixel as a result of the anatomical change.
What is needed are faster, more efficient methods and apparatus for reducing noise in CT imaging applications.